MétaCan
Menu
← Back to cohort
Record W2461401400 · doi:10.1242/jeb.112607

Dateless bees wear better perfume

2015· article· en· W2461401400 on OpenAlexaff
Katie E. Marshall

Bibliographic record

VenueJournal of Experimental Biology · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMatingSet (abstract data type)Mate choiceBiologyEcologyZoologyComputer science

Abstract

fetched live from OpenAlex

Attracting a mate is an expensive endeavour – there are dances to learn, the right outfits to wear and an entire array of vocalizations to perfect. Because males are usually the sex that does the searching, most scientists have focused on the costs that they bear. But in a few species conditions can arise that cause females to invest in finding a mate. In these situations, females have to balance several costs: the potential cost of not producing any offspring if she fails to find a mate, the cost of mating itself and the cost of signalling to attract a mate.To best balance these costs, theory predicts that females should increase their signalling effort the longer that they spend without a mate, as the potential cost of failing to find a mate increases. Leigh Simmons, a researcher from the University of Western Australia, decided to test this hypothesis using the solitary ground-nesting Dawson's bee. As males only search for females immediately after the females emerge as adults, females who get left out during the first pass must find ways later to attract the attention of males by using a particular blend of chemicals found on their cuticles that function as a pheromone perfume. Would females that had failed to mate the first time round invest more in the chemical composition of their cuticle chemicals to attract a male on the rebound?First, Simmons nabbed 50 female bees right as they emerged from their burrows. He froze one set immediately, then isolated another set in a sort of bee nunnery away from males for a day and then froze them. Finally, he unleashed the remaining females to a group of male bees, allowing some to mate and perform their post-copulatory courtship behaviour, while others only mated (to isolate the effects of insemination on cuticle chemistry); and a final group was allowed to mate and nest after. This gave him five groups across which to compare the composition of their cuticle covering: one that was young and unmated, another that was older and unmated, one that had mated but not had post-copulatory courtship, one that had mated and had post-copulatory courtship, and one that was allowed to have all the normal mating behaviours and then nest afterwards. Simmons then used hexane to dissolve the chemicals from the cuticle of each bee before using gas chromatography to identify and quantify each chemical.He found a total of 21 compounds that dissolved out of the female bees’ cuticles, and the composition of the cuticle chemicals extracted from the female bees that had been unmated for a day had altered significantly. Meanwhile, the cuticle components of the bees that had mated (either with or without post-copulatory courtship) were more like those of the freshly emerged bees, and the chemical composition of the nesting bees’ cuticles differed from that of all of the other groups. So, the unmated female bees had altered the composition of their cuticle chemicals, while mated female bees did not.Not a lot is known about the costs of pheromone signalling or the mechanisms by which females can change their signalling, but in desperate times, it seems that at least female Dawson's bees break out their best perfume to find that lucky guy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0190.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.084
GPT teacher head0.276
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Experimental Biology→Same topicPlant and animal studies→French-language works237,207→